Typically, when conducting a ‘survey’ of participants of a population to gather their input on a topic, two different types of participant responses are elicited: quantitative responses and qualitative responses. A quantitative response is a close-ended response, such as a multiple choice, numeric style, or yes/no response. A qualitative response is an open-ended, comment style response, where the participant has freedom to textualize his or her own personal ideas and is not constrained by pre-determined answers. Accordingly, eliciting qualitative responses may have substantial benefits over quantitative responses in that qualitative responses can provide more precise information about participant thoughts.
However, there are well known limitations with handling, evaluating, or otherwise processing qualitative responses, as compared to quantitative responses. This problem of processing qualitative responses generalizes to dealing with any amount of gathered textual or quantitative information that could be acquired by a survey or by other means (e.g., transcripts of phone conversations).
There is no easy way to aggregate or summarize qualitative textual answers in the way that numeric data can be processed with well known techniques. Conventional techniques for this aggregation are complex and resource-consuming.
Accordingly, a need in the art exists for improved techniques for processing qualitative responses or other information.